Voluntary reaction time and long-latency reflex modulation
Bibliographic record
Abstract
Stretching a muscle of the upper limb elicits short (M1) and long-latency (M2) reflexes. When the participant is instructed to actively compensate for a perturbation, M1 is usually unaffected and M2 increases in size and is followed by the voluntary response. It remains unclear if the observed increase in M2 is due to instruction-dependent gain modulation of the contributing reflex mechanism(s) or results from voluntary response superposition. The difficulty in delineating between these alternatives is due to the overlap between the voluntary response and the end of M2. The present study manipulated response accuracy and complexity to delay onset of the voluntary response and observed the corresponding influence on electromyographic activity during the M2 period. In all active conditions, M2 was larger compared with a passive condition where participants did not respond to the perturbation; moreover, these changes in M2 began early in the appearance of the response (∼ 50 ms), too early to be accounted for by voluntary overlap. Voluntary response latency influenced the latter portion of M2, with the largest activity seen when accuracy of limb position was not specified. However, when participants aimed for targets of different sizes or performed movements of various complexities, reaction time differences did not influence M2 period activity, suggesting voluntary activity was sufficiently delayed. Collectively, our results show that while a perturbation applied to the upper limbs can trigger a voluntary response at short latency (<100 ms), instruction-dependent reflex gain modulation remains an important contributor to EMG changes during the M2 period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".